The importance of transparency in AI systems
AI developers should be transparent about the purpose, scope, and operation of their AI systems, including the algorithms, data inputs, and decision-making processes. Transparency is crucial to ensure that AI is developed with clarity on its potential benefits and risks. It helps to ensure that ethical issues are considered. AI systems are only deemed as ethical as the algorithms and data utilized for creating them.
Transparency further helps to build trust within automated systems. Besides these benefits, transparency facilitates public scrutiny of the development process, thus helping to identify any potential problems before they become major issues. The other benefit is that transparency results in the responsible development of AI. An accessible and open development process ensures that developers are held accountable for any potential issues that emerge. This can aid in preventing the development of biased or discriminatory AI systems.
Description of how lack of transparency can lead to harmful outcomes
The lack of transparency in AI makes it difficult for application in the real world, thus rendering the work worthless when the results are not reproducible. The lack of transparency leads to discomfort that deters the acceptance and deployment of new technology. The “black box” problem or opaque nature of how AI makes decisions makes people become distrustful of actions taken.
In an organization, the lack of transparency in AI hurts the overall trust in AI. Failing to disclose the source code, mathematical model, a machine learning model’s inputs and outputs or the training data can make organizations be vulnerable to losing customer trust. A machine learning system that behaves unfairly in terms of economic status, race, or gender can result in the loss of trust with existing clients. Such organizations can also be subjected to regulatory and public scrutiny, thus potentially running their brand. The other concern companies may grapple with about transparency is that in the midst of public backlash because of alleged discrimination or bias, they may have to stop the use of their systems or even delay deployment, leading to disruptions in business operations.
Examples of harmful outcomes resulting from lack of transparency in AI systems
Amazon: The company had to stop using its machine-based hiring tool since the underlying model favored male applicants for technical positions, leading to intense criticism that Amazon was propagating gender disparity in technology.
Google: The most renowned and studied black box algorithm is the one that governs Google searches. Web positioning is influenced by certain factors, but no one outside the company understands exactly all parameters that impact a web page’s ranking, nor these parameters’ percentage of weighting. In 2019, Google employees were in open revolt over AI and transparency. Various Google employees petitioned the organization to quit Project Dragonfly, a search engine for the Chinese government that has previously been under fire from both politicians and human rights groups for its supposed role in internet censorship. One of the major issues with Dragonfly was that Google would be agreeing to share the data of people who made searches such as democracy, human rights, or Dalai Lama with the Chinese authorities.
Recommendations for ensuring transparency in AI systems
Explainable XAI Technology: Transparency in AI can be attained using explainable AI (XAI) technology. XAI offers a way to explain the actions and decisions of AI systems in a way that people can understand. This ensures that AI systems make decisions ethically and responsibly. In addition, XAI can aid in identifying potential AI system biases, ensuring that they are addressed before they become major issues. In other words, Explainable AI helps to account for why an AI system made a certain decision.
Audit Trails: Using audit trails is another way of promoting transparency. They record the actions and decisions taken by AI systems, facilitating the review of these actions and decisions. This helps to ensure that AI systems operate ethically and responsibly.
Open Sources AI Technology: It allows for the inspection and review of AI systems, facilitating the identification of potential problems and AI systems’ improvement.
Data minimization: One of the data privacy principles emphasizes the need to collect and process only the minimum quantity of data needed for AI purposes. This helps to minimize the risks of data misuse, discrimination, and breaches. The data collected should also be accurate, updated, and relevant. Thus, it is important to create a non-discriminatory, equal, and accurate AI model by having an unbiased dataset.
Data Explainability: This principle should be embedded into AI practices and decisions to provide meaningful and clear explanations of how the AI system functions and what it does. The criteria, logic, and evidence of the AI decision-making and outcomes should be provided. They should also be made accessible and understandable to data stakeholders. It is also important that companies enable review and feedback mechanisms, and allow for oversight or human intervention when necessary. Data explainability can help to improve the accountability and transparency of an AI system. More importantly, humans should be kept in the loop by ensuring that they participate in revisiting decisions to deter errors and prejudices that usually affect AI projects.